000162433 001__ 162433
000162433 005__ 20251017144614.0
000162433 0247_ $$2doi$$a10.1038/s41746-025-01890-x
000162433 0248_ $$2sideral$$a145008
000162433 037__ $$aART-2025-145008
000162433 041__ $$aeng
000162433 100__ $$aCamacho-Gomez, Daniel$$uUniversidad de Zaragoza
000162433 245__ $$aPhysics-informed machine learning digital twin for reconstructing prostate cancer tumor growth via PSA tests
000162433 260__ $$c2025
000162433 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162433 5203_ $$aExisting prostate cancer monitoring methods, reliant on prostate-specific antigen (PSA) measurements in blood tests often fail to detect tumor growth. We develop a computational framework to reconstruct tumor growth from the PSA integrating physics-based modeling and machine learning in digital twins. The physics-based model considers PSA secretion and flux from tissue to blood, depending on local vascularity. This model is enhanced by deep learning, which regulates tumor growth dynamics through the patient’s PSA blood tests and 3D spatial interactions of physiological variables of the digital twin. We showcase our framework by reconstructing tumor growth in real patients over 2.5 years from diagnosis, with tumor volume relative errors ranging from 0.8% to 12.28%. Additionally, our results reveal scenarios of tumor growth despite no significant rise in PSA levels. Therefore, our framework serves as a promising tool for prostate cancer monitoring, supporting the advancement of personalized monitoring protocols.
000162433 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T50-23R$$9info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101018587-ICoMICS$$9info:eu-repo/grantAgreement/ES/MICINN/PLEC2021-007709
000162433 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
000162433 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000162433 700__ $$aBorau, Carlos
000162433 700__ $$0(orcid)0000-0002-9864-7683$$aGarcia-Aznar, Jose Manuel$$uUniversidad de Zaragoza
000162433 700__ $$0(orcid)0000-0002-1878-8997$$aGomez-Benito, Maria Jose$$uUniversidad de Zaragoza
000162433 700__ $$aGirolami, Mark
000162433 700__ $$0(orcid)0000-0002-2901-4188$$aPerez, Maria Angeles$$uUniversidad de Zaragoza
000162433 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000162433 773__ $$g8, 1 (2025), 485 [10 pp.]$$pnpj digit. med.$$tnpj digital medicine$$x2398-6352
000162433 8564_ $$s1552698$$uhttps://zaguan.unizar.es/record/162433/files/texto_completo.pdf$$yVersión publicada
000162433 8564_ $$s2946939$$uhttps://zaguan.unizar.es/record/162433/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000162433 909CO $$ooai:zaguan.unizar.es:162433$$particulos$$pdriver
000162433 951__ $$a2025-10-17-14:18:40
000162433 980__ $$aARTICLE